Looking to build a strong career in modern data engineering? At Openminds Technologies, our Azure Databricks course in Hyderabad helps you truly understand how big data is processed, analyzed, and transformed in real world projects. From working with large datasets and Spark fundamentals to building scalable data pipelines on Azure, this course focuses on practical skills used in today’s data driven companies.
Expert Trainer | Hands-on Training
duration
• What is the use of Databricks?
• Spark Architecture
• Workspace
• Types of Clusters and Runtimes
• Notebooks
• Jobs
• Upload files into DBFS
• dbutils.fs
• dbutils.data
• dbutils.data.summarize
• dbutils.fs.cp
• dbutils.fs.head
• dbutils.fs.mkdirs
• dbutils.notebook.run()
• dbutils.notebook.exit()
• dbutils.data
• dbutils.data.summarize
• dbutils.fs.cp
• dbutils.fs.head
• dbutils.fs.mkdirs
• Dbutils.widgets.combobox
• Dbutils.dropdown.dropdown
• dbutils.dropdown.multiselect
• dbutils.dropdown.text
• dbutils.dropdown.get
• dbutils.dropdown.getArgument
• dbutils.widgets.remove
• Create mount point using Account Key
• Create mount point using SAS Token
• Connect ADLS Gen2 to Databricks
• Delete or Unmount Mount Points in Azure Databricks
• mounts() & refreshMounts() commands of File system
Utilities in Azure Databricks
• Update Mount Point(dbutils.fs.updateMount()) in Azure
Databricks
• Azure Key vault backed scopes
• Databricks-backed scopes
• Pass Parameter to Notebook from ADF Pipeline
• Send Exception or Error message from Pipeline to
Notebook
• Send parameters from Notebook to ADF Pipelines
• Time Travel/Versioning in Delta Lake
• RESTORE
• Get Delta Lake History
• Vacuum Command in Delta Lake
• Merge Command in Delta Lake
• Schema evaluation in Delta Lake
• Change Data feed in Delta Lake
• Z-Order index
• Table constraints
• Runbooks
• Run SQL Commands from Runbook
• Run Pipeline from Runbook
• Optimize performance with caching on Azure
Databricks
• Dynamic file pruning
• Low shuffle merge
• Adaptive query execution
• predictive IO
• Cost-based optimizer
• Auto optimize
• Higher-order functions
• Isolation levels
• column mapping
• Parallelize
• Collect()
• Repartition vs coalesce
• Broadcast variables
• Accumulator
• RDD Transformations
• Transformations Actions
• Create an Empty DataFrame
• Create Empty DataFrame with Schema
• Convert Empty RDD to DataFrame
• show()/Display()
• StructType() and StructField()
• Column class
• Select()
• WithColumn()
• WithColumnRenamed()
• Where() and Filter()
• Drop() and DropDuplicates()
• orderBy() and sort()
• groupBy()
• Join()
• union() and unionAll()
• unionByName()
• map()
• flatmap()
• fillna() and fill()
• pivot()
• partitionBy()
• mapType()
• foreach()
• User Defined Functions
• Aggregate functions
• Window functions
• Date and Timestamp functions
• JSON functions
• Read and write CSV File
• Read and Write Parquet File
• Read and Write JSON File
• Read Hive Table
• Save to Hive Table
• Read JDBC in parallel
• Query Database Table
• Read and Write SQL Server
• Read JDBC Table
• when()
• expr()
• lit()
• split()
• concat_ws()
• substring()
• translate()
• regexp_replace()
• to_timestamp()
• to_date()
• date_format()
• struct()
• countDistinct()
• sum(), avg()
• row_number()
• rank()
• dense_rank()
• from_json()
• to_json()
• json_tuple()
• get_jason_object()
• schema_of_json()
• array()
• collect_list()
• collect_set()
• create_map()
• map_keys()
• map_values()
• months_between()
• explode()
• array_constraints()
• datediff()
• Autoloader → Ingest data efficiently
into Delta tables.
• Unity Catalog → Govern and secure
those Delta tables across
workspaces/clouds.
• Mounting → Provide simple,
persistent access to external
storage locations.
• CDC (Change Data Capture) in
Databricks
• CDC Approaches in Databricks
• Implementing CDC in Databricks
with Delta Lake
• Medallion Architecture in Databricks
• What is it?
• Layers in Medallion Architecture
• Key Benefits
UPI, Net Banking, Debit/Credit Cards, and EMI options are available.
Generally, recordings are accessible for 3 to 6 months post-course completion.
